1. Field of the Invention
The present invention relates generally to modeling power applications and more particularly to a system and method for real-time modeling of plant control for a wide range of physical and information processing applications such as combustion control, SCR optimization, FGD optimization, fuel blending, ash control, precipitator optimization, equipment diagnostics for mills, blowers, fans, transformers and the like as well as power grid modeling for optimization of power flow and trading. All these applications models can be coupled with optimization in a power plant or distribution grid.
2. Description of the Prior Art
Computing technology is advancing at a rapid pace permitting more powerful algorithms and complex strategies to be implemented to more efficiently control a process. Increasingly a shift in automation is occurring from controlling mundane repetitive tasks to controlling those of higher order complexity that previously would have to have been simplified by human operators to achieve timely response. The increasingly complex tasks can be automated to assist, complement or take direct or indirect control over process and business operations that before were only manually adjusted.
Process control, especially power plant control, involves both continuous processes (e.g. heating water) and discrete processes (e.g. turning a motor on/off). Generally, this involves using input data from physical sensors or manual input to determine the controller's reaction to achieve a goal or output. Such input data may be used directly from sensors or manual inputs, or some of this data may be pre-processed in some form and collected from data bases, software programs, memory or registers in hardware or other ways of moving and transforming raw data into desired information. The goal may be direct, such as control a temperature, pressure, flow, specific octane, motor, or in business, initiate a trade or the like, or it may more abstract, such as maximizing profit or the life of equipment.
Process control requires the representation and movement of information, a model of the data, and a way to evaluate the success of achieving the goal or objective. With more complex process control, the latter is often termed in the art optimization. Process control can be a physical process or an abstract process. In a power plant, many times it is a direct physical process that controls the operation with possibly abstract goals of keeping costs down, maximizing output or profit, extending the useful life of equipment and the like.
Simple models can often be represented by regression, a simple set of physical relationships or a set of equations. As the data becomes less accurate, or the relationships are unknown, empirical models, such as fuzzy logic and neural networks may be used. Many different strategies are used to train these parametric models (neural networks and regression models) and then to select the models to be used.
One of the challenges is that often the process is changing, evolving, being modified, or is otherwise is dynamic. Also, many states may exist that are rarely encountered or that the model has not previously encountered. It becomes a challenge to build models and optimizers that can cover such a wide range of states and achieve close to a truly optimized target. Also, disparate data sources may be required for building models and optimizers.
It is also many times a challenge to gather sufficient data to train models. Traditional methods of operator directed testing and automated testing can be very expensive and time consuming, and may not adequately address the issue of the change process. Prior art methods including model swapping was pioneered by Pegasus Technologies in 1996 where one model is used for control, while another model is being trained on new data generated by the model in control. When a particular swapping criteria is met, the indirect model is moved into the place or the control model.
Prior art solutions tend to be focused on a particular problem and then designing a tool or toolset that can be adapted to that problem. Often these solutions are dynamically adaptive, i.e. responsive to changes in the system behavior in real-time or close to real-time. Generally they require the use of multiple models, necessitating either a dynamic switching of models or an averaging of model results.
In U.S. Pat. Nos. 5,167,009 (and 5,224,203), Skeirik teaches the use of on-line process control using neural networks with data pointers for direct control. In U.S. Pat. No. 5,111,531, Grayson et. al. teaches the use of neural networks as indirect controllers.
There have been several improvements in the art to handle particular classes of problems. Examples include: U.S. Pat. No. 4,979,126 which combines several previously independent aspects of neural networks, including supervised learning, unsupervised learning along with functional link enhancement and U.S. Pat. No. 5,282,261 which includes a usage of neural networks to predict product properties or values in place of a directly measured variable.
Other prior art modifies the neural network component to achieve different type of information processing. For example, in U.S. Pat. No. 6,363,289, Keeler et. al, teach how to use networks to be trained on the residual error left after subtracting from the actual state variable. In U.S. Pat. No. 7,219,087, Panfilov, et. al. teach the use of a Fuzzy Neural Network for controlling a power plant. In U.S. Pat. No. 6,805,099 Malaczynki, et. al teach how to use wavelet transforms to extract critical signal features for neural net combustion sensing. In U.S. Pat. Nos. 7,164,954 (and 7,194,320), LeFebvre et al. teach implementing a indirect controller using a committee of models, whereby the user can keep a number of distinct models and either average the outputs or swap models when performance is poor the models in use.
It would be advantageous to have models that are more flexible that adapt to incoming data from any source and quality. Models that recognize data problems like data sparsity and automatically adjust internal weights would offer continuous improvement over model swapping and non-dynamic model configurations. In addition, the ability to mix model types, both in structure for neural networks and in model type in general, would enable more powerful and accurate data interrogation and optimization strategies.
In addition, the creation of a new criteria for measuring the optimization capability, the Directional Change Correlation index, allows for models that perform better under a much wider set operating conditions than previous advances and provide a new method of evaluating models suitability for control. Using any of these advances, in part or in whole, would advance the are of optimization. In summary, these adaptations should allow for higher fidelity models that then permit a better optimization using the traditional techniques.